Kevin P. Cross, Ph.D. Leadscope, Inc. 1393 Dublin Road, Columbus , OH, USA

1. InSilico Methods as Translational Tools for Supporting the Safety Assessment of Natural Health Products • Kevin P. Cross, Ph.D. Leadscope, Inc. 1393 Dublin Road, Columbus, OH, USA

2. Collaboration • Research conducted under an approved CRADA, and Research Collaboration Agreement between U.S. FDA and Leadscope Inc. with: • Luis G. Valerio, Jr., Ph.D., Science and Research Staff, CDER • Leadscope and the FDA have been collaborating on developing in silico predictive QSAR models for 7 years. • Currently there are Research Collaboration Agreements with both FDA/CDER and CFSAN • There are regulatory needs and active use of QSAR models for assessing the safety of food ingredients and drug impurities • This talk will discuss how those research activities have evolved into translational tools for better assessing the safety of FDA regulated ingredients with attention to botanicals, dietary supplements, etc.

3. Disclaimer The views expressed in this presentation are those of the author; this talk is not an official U.S. FDA presentation, guidance or policy statement.

4. Natural Health ProductSafety Assessment “A Perfect Storm?” • Globally chemically diverse compounds – compared to drugs • Many constituents per product – requiring analysis • Many unknown active constituents – requiring analysis • Data gaps in safety analysis – cost and incentive • Inconsistent and equivocal results (rodent, human) • Variation in dose - subspecies, part, extract, manufacturer, lot • Food matrix effects - dosage, absorption, toxicity • Quality Control and adulteration – non-GMP, synthetic additions • Metabolism-dependent toxicity – cytochrome P450 effects • Delayed onset of adverse symptoms – days to months • Co-administration – herb-herb, herb-drug interactions • Under-reporting of adverse events – manufacturers and doctors • Idiosyncratic effects – genetic basis, culture, immunology-mediated • “Leaky” Regulations – DSHEA versus the Delany Clause

5. Natural Health ProductSafety Assessment“A Perfect Storm?” • References: • Jordan, S.A., Tox and App Pharm (2010) 243: 198-216 • Teschke, R., et. al., Expert OpinDrgSaf, (2013), early online • National Health and Nutrition Examination Survey 1999–2012 (2013) • NIH NCC Roundtable Meeting on Dietary Supplement-Drug Interactions March 27, 2012 • Rietjens, I.M.C.M, Mol Nut Food Res (2010) 54(2): 175-185 • Olsen, L.R., et. al., Chem Res Tox (2011) 24: 992-1002 • Reddy, K.R., et. al., Aliment Pharm Ther (2013) 37: 3-17 • Rietjens, I.M.C.M, Tox Letters (2008) 180: 131-136 • Fourches, D., el. al., Chem Res Tox (2010) 23: 171-183 • Stickel, F., et. al., Liver International (2011) 595-605 • Estes, J.D., et. al., Arch Surg (2003) 138: 852-858 • Valerio, L. G., et. al., MolNutr Food Res (2010) 53: 186-194 • Clayton, N.P., et. al., ExpTox Path (2007) 58: 223-236 • Smith, D. A., et. al., Chem Res Tox (2006) 19: 1570-1579 • Gurley, B.J., et. al., Clin Pharm Ther (2005) 77: 415-417 • Denham, A., et. al., J. Alt. Complement Med (2002) 8: 237-263 • Schiliter, B., et. al., Risk Asses of Phyto Foods Symp 2010, Wiley • Arvidson, K,.B., et. al., (2008) ToxMech Methods 18: 229-242

6. Headlines of the Last Month • FDA Warns Consumers About Fraudulent Health Products, HealthDay (3/7, Preidt) • FDA Issues Warning On Fake "Miracle Cures.“,The Hill (3/5, Wilson) • FDA Reviewers: Fish Hormone Use May Be Linked To Cancer Risk , MedPage Today(3/5, Gever) • Study: Approximately 30 Percent Of Chemotherapies Are Prescribed Off-Label, Reuters(2/20, Seaman) • FDA Issues Warning On Fake Flu Therapies Sold Online, The Wall Street Journal (2/16, A2, Rockoff) • Synthetic Marijuana Use Linked To Kidney Problems, The AP (2/14, Stobbe) • FDA Seizes Supplements Containing Banned Ingredient, The AP (2/15) • FDA Warns Company Over Unapproved Nasal Spray, USA Today (1/31, Weise) Extracted from Drug Information Agency daily briefings Jan 31- Mar 7, 2013

7. Outline • Natural Health Products • in silico methods including computational toxicology can serve to address data gaps during safety evaluations for regulatory and consumer product safety • Why use in silico-based strategies to assess their toxicity? • In what specific areas can in silico-based strategies help? • What in silicopredictive models are in use at FDA? • Leadscope technology • Genetic toxicity and carcinogenicity models • Clinical cardiovascular adverse effects models • Case examples

8. At a Glance – Regulatory Framework in the U.S. • In October 1994, the Dietary Supplement Health and Education Act (DSHEA) was signed into law by President Clinton. Before this time, dietary supplements were subject to the same regulatory requirements as were other foods. This new law, which amended the Federal Food, Drug, and Cosmetic Act, created a new regulatory framework for the safety and labeling of dietary supplements. • Under DSHEA, a firm is responsible for determining that the dietary supplements it manufactures or distributes are safe and that any representations or claims made about them are substantiated by adequate evidence to show that they are not false or misleading. This means that dietary supplements do not need approval from FDA before they are marketed. Except in the case of a However, for new dietary ingredients now marketed before Oct 1994, pre-market review for safety data and other information is required by law. a firm does not have to provide FDA with the evidence it relies on to substantiate safety or effectiveness before or after it markets its products.

9. General Problem- Data Gaps • Botanicals are complex mixtures of chemicals, which often contain ‘active’ constituents that have little or no toxicological data • Chemicals of natural origin with vast structural diversity (e.g. phytochemicals) are being discovered at an accelerating pace • Present a data-poor situation in assessment of toxic potential • Prime example of a need for efficient prioritization and toxicity screening methods 9

10. Why In Silico?

11. In what specific areas can in silico-based strategies help?

12. FDA Critical Path Initiative(March 2004) • Identifies need to address drug development issues • Reduce drug product attrition and improve chances for success • Emphasizes industry – FDA interactions • Use science to create new tools to get better answers for demonstrating safety and effectiveness, faster time frames, more certainty, and lower costs • Product development toolkit* • New scientific and technical methods • Animal or computer-based predictive models • Biomarkers for safety and effectiveness • New clinical evaluation techniques *Valerio, L.G., Human Gen (2011) 5(3) 200-207

13. FDA-related Guidance Involving QSAR • The ICH M7 Guideline on Assessment and Control of DNA Reactive (Mutagenic) Impurities in Pharmaceuticals to Limit Potential Carcinogenic Risk. • Status: Step 2. Draft released for public comment. • Allows submission of QSAR AMES prediction results in place of genotoxic testing for certain types of impurities under a threshold level. • Two (Q)SAR prediction methodologies that complement each other should be applied. One methodology should be expert rule-based and the second methodology should be statistical-based. • (Q)SAR models utilizing these prediction methodologies should follow the validation principles set forth by the Organization for Economic Co-operation and Development (OECD). • The outcome of any computer system-based analysis should be reviewed with the use of expert knowledge http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Multidisciplinary/M7/M7_Step_2.pdf Valerio L.G. Jr, Cross K.P., ToxicolApplPharmacol. (2012) 260(3):209-21

14. Leadscope Model Suites Developed Under Collaboration with the FDA *not yet commercially available SOT 2013 Posters FDA CDER Division of Drug Safety Research: 1Stavitskaya, L., Development of Improved Salmonella Mutagenicity QSAR Models Using Structural Fingerprints of Known Toxicophores 2Zhu, X., and Kruhlak,N. L., Construction of a Human Hepatotoxicity Database for QSAR Modeling

15. Clinical QT Model Development • Only FDA clinical data adhering to ICH E14 clinical standards on evaluating proarrythmic risk was gathered* • All data reviewed by FDA/CDER’s QT Interdisciplinary Review Team • Clinical long QT and high-risk Torsade de Pointes QSAR models were built using the Leadscope Predictive Data Miner1 • QSAR models were built independent of molecular mechanism to avoid limitations with existing tests (e.g. hERG) • Training sets are small (187) so coverage is currently limited • QSAR models are being tested at the FDA to support safety decision-making – they are not required during the review process • Funded by the FDA Critical Path Initiative *ICH Industry Guidance E14 Clinical Evaluation of QT/QTc Interval Prolongation and ProarrhythmicPotential for Non-Antiarrhythmic Drugs, October 2005, U.S. FDA 1Valerio et al, Expert Opin. Drug Metab. Toxicol, March 2013 accepted

16. Cardiac Safety Translational Tools FDA Critical Path Project Development of Clinical based QT Prolongation and Torsade de Pointes Models Valerio et al, Expert Opin. Drug Metab. Toxicol, March 2013 accepted 16

17. External Validation Test of Predictive Performance of the Clinical QT Prolongation Model

18. Computational Assessment of Liver Toxicity for Botanicals Used in Menopausal Symptom Relief Wang, Y., et. al., RegToxPharma (2011) 59: 111-124

19. Computational Assessment of Liver Toxicity for Botanicals Used in Menopausal Symptom Relief • Botanical constituents collected from public databases for black cohosh, chasteberry, hops, and red clover* • Constituent compounds present in relevant parts of plant extract identified • Hepatotoxicity predictions made using five (Q)SAR software applications • Developed a “positive” prediction list • Identification of structural features contributing to “positive” predictions *Betz et al. Black Cohosh. Considerations of Safety and Benefit. Nutrition Today, (2009 ) 44(4), 155-162.

20. Identification of Constituents and Prediction of Liver Toxicity in Botanicals Wang, Y., et. al., RegToxPharma (2011) 59: (111-124)

21. Analysis of a (Q)SAR Prediction Calculated Properties Model features used to make this prediction Structure with model features highlighted Feature contribution to predicted positive probability • Structural features (applied model features and contributions) • Calculated properties (LogP, molecular weight, polar surface area, number of rotational bonds, etc.)

22. mitigating contributions positive and negative contributions positive contributions predicted probability summarizes atom contribution in the model percentage contribution from structural features and properties Identifying Structural Features Contributing to Positive Predictions Example: 3-methoxy-7-O-beta-D-glucoside quercetin (Red Clover) Endpoint: Enzyme Release *Analysis using Leadscope prediction Explain functionality

23. Computational Assessment of Carcinogenicity and Liver Toxicity of Kava

24. Major Constituents of Kava Lactones Alkaloids Flavokavains • Olsen, L. R., Grillo, M. P., Skonberg, C., Chem. Res. Toxicol 2011, 24, 992-102

25. Read-Across Data for Kava (flavokavain C)

26. NIEHS 2-Year Rodent Carcinogenicity Study of Dietary Herbal Medicines(published December 2012) Dunnick, J.K., Nyska, A., Tox Pathology, 2012, 00: 1-13

27. Kava Rodent Carcinogenicity QSAR Prediction Results

28. Kava Adverse Events Reporting Liver QSAR Prediction Results

29. Conclusions • In silico methods can be applied to natural health products to support safety analysis by: • data mining existing databases • using QSAR predictions to fill data gaps • screening large numbers of constituents without requiring synthesis • prioritizing constituents for further safety analysis • helping to understand chemical toxicity and mechanisms • In silico methods are being used in a regulatory setting to: • analyze drug impurities in drug applications containing QSAR results • support review of drug safety analysis (FDA Div Drug Safety Research) • support safety assessments of food ingredients and indirect additives

30. The Future In silico methods will continue to provide better support for safety assessment of Natural Health Products: • by providing supporting chemical and mechanistic evidence for predictions to help define toxicity pathways1 • supporting a broader set of diverse compound predictions • through integration with other data (in vitro) in decision support2 • by continually collecting more data for model revisions • by supporting creation of chemical categories of toxicity by common mechanism and class3 1Collins, F.S., et. al., Science (2008) 319: 906-907 2Committee on Toxicity Testing and Assessment of Environment Agents, National Research Council 2007 3Rietjens, I.M.C.M, Tox Letters (2008) 180: 131-136

31. Thank You! kcross@leadscope.com

32. Predictive Feature Analysis Across Several Constituents Example: Red Clover constituents

33. Who has the responsibility for ensuring that a dietary supplement is safe? • By law (DSHEA), the manufacturer is responsible for ensuring that its dietary supplement products are safe before they are marketed. Unlike drug products that must be proven safe and effective for their intended use before marketing, there are no provisions in the law for FDA to "approve" dietary supplements for safety or effectiveness before they reach the consumer. Under DSHEA, once the product is marketed, FDA has the responsibility for showing that a dietary supplement is "unsafe," before it can take action to restrict the product's use or removal from the marketplace. However, manufacturers and distributors of dietary supplements must record, investigate and forward to FDA any reports they receive of serious adverse events associated with the use of their products that are reported to them directly. FDA is able to evaluate these reports and any other adverse event information reported directly to us by healthcare providers or consumers to identify early signals that a product may present safety risks to consumers.

34. When must a manufacturer or distributor notify FDA about a dietary supplement it intends to market in the U.S.? • The Dietary Supplement Health and Education Act (DSHEA) requires that a manufacturer or distributor notify FDA if it intends to market a dietary supplement in the U.S. that contains a "new dietary ingredient." The manufacturer (and distributor) must demonstrate to FDA why the ingredient is reasonably expected to be safe for use in a dietary supplement, unless it has been recognized as a food substance and is present in the food supply. • There is no authoritative list of dietary ingredients that were marketed before October 15, 1994. Therefore, manufacturers and distributors are responsible for determining if a dietary ingredient is "new", and if it is not, for documenting that the dietary supplements its sells, containing the dietary ingredient, were marketed before October 15, 1994.